Enhancing weak signal transmission through a feedforward network

The ability to transmit and amplify weak signals is fundamental to signal processing of artificial devices in engineering. Using a multilayer feedforward network of coupled double-well oscillators as well as Fitzhugh-Nagumo oscillators, we here investigate the conditions under which a weak signal received by the first layer can be transmitted through the network with or without amplitude attenuation. We find that the coupling strength and the nodes' states of the first layer act as two-state switches, which determine whether the transmission is significantly enhanced or exponentially decreased. We hope this finding is useful for designing artificial signal amplifiers.

Modelling shared attention through relational reinforcement learning

Shared attention is a type of communication very important among human beings. It is sometimes reserved for the more complex form of communication being constituted by a sequence of four steps: mutual gaze, gaze following, imperative pointing and declarative pointing. Some approaches have been proposed in Human-Robot Interaction area to solve part of shared attention process, that is, the most of works proposed try to solve the first two steps. Models based on temporal difference, neural networks, probabilistic and reinforcement learning are methods used in several works. In this article, we are presenting a robotic architecture that provides a robot or agent, the capacity of learning mutual gaze, gaze following and declarative pointing using a robotic head interacting with a caregiver. Three learning methods have been incorporated to this architecture and a comparison of their performance has been done to find the most adequate to be used in real experiment. The learning capabilities of this architecture have been analyzed by observing the robot interacting with the human in a controlled environment. The experimental results show that the robotic head is able to produce appropriate behavior and to learn from sociable interaction.

Network-based high level data classification

Traditional supervised data classification considers only physical features (e. g., distance or similarity) of the input data. Here, this type of learning is called low level classification. On the other hand, the human (animal) brain performs both low and high orders of learning and it has facility in identifying patterns according to the semantic meaning of the input data. Data classification that considers not only physical attributes but also the pattern formation is, here, referred to as high level classification. In this paper, we propose a hybrid classification technique that combines both types of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features or class topologies, while the latter measures the compliance of the test instances to the pattern formation of the data. Our study shows that the proposed technique not only can realize classification according to the pattern formation, but also is able to improve the performance of traditional classification techniques. Furthermore, as the class configuration's complexity increases, such as the mixture among different classes, a larger portion of the high level term is required to get correct classification. This feature confirms that the high level classification has a special importance in complex situations of classification. Finally, we show how the proposed technique can be employed in a real-world application, where it is capable of identifying variations and distortions of handwritten digit images. As a result, it supplies an improvement in the overall pattern recognition rate.

Network-based stochastic semisupervised learning

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.

A graph-theoretical approach in brain functional networks. Possible implications in EEG studies

Abstract Background Recently, it was realized that the functional connectivity networks estimated from actual brain-imaging technologies (MEG, fMRI and EEG) can be analyzed by means of the graph theory, that is a mathematical representation of a network, which is essentially reduced to nodes and connections between them. Methods We used high-resolution EEG technology to enhance the poor spatial information of the EEG activity on the scalp and it gives a measure of the electrical activity on the cortical surface. Afterwards, we used the Directed Transfer Function (DTF) that is a multivariate spectral measure for the estimation of the directional influences between any given pair of channels in a multivariate dataset. Finally, a graph theoretical approach was used to model the brain networks as graphs. These methods were used to analyze the structure of cortical connectivity during the attempt to move a paralyzed limb in a group (N=5) of spinal cord injured patients and during the movement execution in a group (N=5) of healthy subjects. Results Analysis performed on the cortical networks estimated from the group of normal and SCI patients revealed that both groups present few nodes with a high out-degree value (i.e. outgoing links). This property is valid in the networks estimated for all the frequency bands investigated. In particular, cingulate motor areas (CMAs) ROIs act as ‘‘hubs’’ for the outflow of information in both groups, SCI and healthy. Results also suggest that spinal cord injuries affect the functional architecture of the cortical network sub-serving the volition of motor acts mainly in its local feature property. In particular, a higher local efficiency El can be observed in the SCI patients for three frequency bands, theta (3-6 Hz), alpha (7-12 Hz) and beta (13-29 Hz). By taking into account all the possible pathways between different ROI couples, we were able to separate clearly the network properties of the SCI group from the CTRL group. In particular, we report a sort of compensatory mechanism in the SCI patients for the Theta (3-6 Hz) frequency band, indicating a higher level of “activation” Ω within the cortical network during the motor task. The activation index is directly related to diffusion, a type of dynamics that underlies several biological systems including possible spreading of neuronal activation across several cortical regions. Conclusions The present study aims at demonstrating the possible applications of graph theoretical approaches in the analyses of brain functional connectivity from EEG signals. In particular, the methodological aspects of the i) cortical activity from scalp EEG signals, ii) functional connectivity estimations iii) graph theoretical indexes are emphasized in the present paper to show their impact in a real application.

Roof modular system in wood and particle board (OSB) to rural construction

Wood is a material of great applicability in construction, with advantageous properties to form various structural systems, such as walls and roof. Most of the roof structural systems follow models that have remained unchanged for a long time. A roof modular system in distinguished materials is proposed: reforested wood (Pine), oriented strand board (OSB) and roof tiles made of recycled long-life packaging material in order to be applied in rural construction. In this alternative, besides the benefit of giving destination packages with long-life thermal comfort, it also highlights the use of reforestated wood being the cultivation of such species that provides incentive for agribusiness. The structural performance of this alternative was evaluated through computer modeling and test results of two modular panels. The analysis is based on the results of vertical displacements, deformations and stresses. A positive correlation between theoretical and experimental values was observed, indicating the model's feasibility for use in roof structures. Therefore, the modular system represents a solution to new architecture conceptions to rural construction, for example, storage construction, cattle handling and poultry, with benefits provided by prefabricated building systems.

High-performance switching architectures for optical networks

The need for high bandwidth, due to the explosion of new multi\-media-oriented IP-based services, as well as increasing broadband access requirements is leading to the need of flexible and highly reconfigurable optical networks. While transmission bandwidth does not represent a limit due to the huge bandwidth provided by optical fibers and Dense Wavelength Division Multiplexing (DWDM) technology, the electronic switching nodes in the core of the network represent the bottleneck in terms of speed and capacity for the overall network. For this reason DWDM technology must be exploited not only for data transport but also for switching operations. In this Ph.D. thesis solutions for photonic packet switches, a flexible alternative with respect to circuit-switched optical networks are proposed. In particular solutions based on devices and components that are expected to mature in the near future are proposed, with the aim to limit the employment of complex components. The work presented here is the result of part of the research activities performed by the Networks Research Group at the Department of Electronics, Computer Science and Systems (DEIS) of the University of Bologna, Italy. In particular, the work on optical packet switching has been carried on within three relevant research projects: the e-Photon/ONe and e-Photon/ONe+ projects, funded by the European Union in the Sixth Framework Programme, and the national project OSATE funded by the Italian Ministry of Education, University and Scientific Research. The rest of the work is organized as follows. Chapter 1 gives a brief introduction to network context and contention resolution in photonic packet switches. Chapter 2 presents different strategies for contention resolution in wavelength domain. Chapter 3 illustrates a possible implementation of one of the schemes proposed in chapter 2. Then, chapter 4 presents multi-fiber switches, which employ jointly wavelength and space domains to solve contention. Chapter 5 shows buffered switches, to solve contention in time domain besides wavelength domain. Finally chapter 6 presents a cost model to compare different switch architectures in terms of cost.

Quality of experience in skype video calls

Skype is one of the well-known applications that has guided the evolution of real-time video streaming and has become one of the most used software in everyday life. It provides VoIP audio/video calls as well as messaging chat and file transfer. Many versions are available covering all the principal operating systems like Windows, Macintosh and Linux but also mobile systems. Voice quality decreed Skype success since its birth in 2003 and peer-to-peer architecture has allowed worldwide diffusion. After video call introduction in 2006 Skype became a complete solution to communicate between two or more people. As a primarily video conferencing application, Skype assumes certain characteristics of the delivered video to optimize its perceived quality. However in the last years, and with the recent release of SkypeKit1, many new Skype video-enabled devices came out especially in the mobile world. This forced a change to the traditional recording, streaming and receiving settings allowing for a wide range of network and content dynamics. Video calls are not anymore based on static ‘chatting’ but mobile devices have opened new possibilities and can be used in several scenarios. For instance, lecture streaming or one-to-one mobile video conferences exhibit more dynamics as both caller and callee might be on move. Most of these cases are different from “head&shoulder” only content. Therefore, Skype needs to optimize its video streaming engine to cover more video types. Heterogeneous connections require different behaviors and solutions and Skype must face with this variety to maintain a certain quality independently from connection used. Part of the present work will be focused on analyzing Skype behavior depending on video content. Since Skype protocol is proprietary most of the studies so far have tried to characterize its traffic and to reverse engineer its protocol. However, questions related to the behavior of Skype, especially on quality as perceived by users, remain unanswered. We will study Skype video codecs capabilities and video quality assessment. Another motivation of our work is the design of a mechanism that estimates the perceived cost of network conditions on Skype video delivery. To this extent we will try to assess in an objective way the impact of network impairments on the perceived quality of a Skype video call. Traditional video streaming schemes lack the necessary flexibility and adaptivity that Skype tries to achieve at the edge of a network. Our contribution will lye on a testbed and consequent objective video quality analysis that we will carry out on input videos. We will stream raw video files with Skype via an impaired channel and then we will record it at the receiver side to analyze with objective quality of experience metrics.

Social Networks Analysis

This morning Dr. Risser will introduce you to the basic ideas of social network analysis. You will learn some history behind the study of social networks. Dr. Risser will introduce you to mathematical measures of social networks including centrality measures and measures of spread and cohesion. You will also learn how to use a computer program to analyze social network data

QoE-aware Multiple Path Video Transmission for Wireless Multimedia Sensor Networks

Wireless Multimedia Sensor Networks (WMSNs) promise a wide scope of emerging potential applications in both civilian and military areas, which require visual and audio information to enhance the level of collected information. The transmission of multimedia content requires a minimal video quality level from the user’s perspective. However, links in WMSN communi- cations are typically unreliable, as they often experience fluctuations in quality and weak connectivity, and thus, the routing protocol must evaluate the routes by using end-to-end link quality information to increase the packet delivery ratio. Moreover, the use multiple paths together with key video metrics can enhance the video quality level. In this paper, we propose a video-aware multiple path hierarchical routing protocol for efficient multimedia transmission over WMSN, called video-aware MMtransmission. This protocol finds node-disjoint multiple paths, and implements an end-to-end link quality estimation with minimal over- head to score the paths. Thus, our protocol assures multimedia transmission with Quality of Experience (QoE) and energy-efficiency support. The simula- tion results show the benefits of video-aware MMtransmission for disseminating video content by means of energy-efficiency and QoE analysis.

Developmental self-construction and -configuration of functional neocortical neuronal networks.

The prenatal development of neural circuits must provide sufficient configuration to support at least a set of core postnatal behaviors. Although knowledge of various genetic and cellular aspects of development is accumulating rapidly, there is less systematic understanding of how these various processes play together in order to construct such functional networks. Here we make some steps toward such understanding by demonstrating through detailed simulations how a competitive co-operative ('winner-take-all', WTA) network architecture can arise by development from a single precursor cell. This precursor is granted a simplified gene regulatory network that directs cell mitosis, differentiation, migration, neurite outgrowth and synaptogenesis. Once initial axonal connection patterns are established, their synaptic weights undergo homeostatic unsupervised learning that is shaped by wave-like input patterns. We demonstrate how this autonomous genetically directed developmental sequence can give rise to self-calibrated WTA networks, and compare our simulation results with biological data.

Content Discovery in Wireless Information-Centric Networks

Information-centric networking (ICN) enables communication in isolated islands, where fixed infrastructure is not available, but also supports seamless communication if the infrastructure is up and running again. In disaster scenarios, when a fixed infrastructure is broken, content discovery algorit hms are required to learn what content is locally available. For example, if preferred content is not available, users may also be satisfied with second best options. In this paper, we describe a new content discovery algorithm and compare it to existing Depth-first and Breadth-first traversal algorithms. Evaluations in mobile scenarios with up to 100 nodes show that it results in better performance, i.e., faster discovery time and smaller traffic overhead, than existing algorithms.

Information-Centric Content Retrieval for Delay-Tolerant Networks

The shift from host-centric to information-centric networking (ICN) promises seamless communication in mobile networks. However, most existing works either consider well-connected networks with high node density or introduce modifications to {ICN} message processing for delay-tolerant Networking (DTN). In this work, we present agent-based content retrieval, which provides information-centric {DTN} support as an application module without modifications to {ICN} message processing. This enables flexible interoperability in changing environments. If no content source can be found via wireless multi-hop routing, requesters may exploit the mobility of neighbor nodes (called agents) by delegating content retrieval to them. Agents that receive a delegation and move closer to content sources can retrieve data and return it back to requesters. We show that agent-based content retrieval may be even more efficient in scenarios where multi-hop communication is possible. Furthermore, we show that broadcast communication may not be necessarily the best option since dynamic unicast requests have little overhead and can better exploit short contact times between nodes (no broadcast delays required for duplicate suppression).

Network convergence and QoS for future multimedia services in the VISION project

The emerging use of real-time 3D-based multimedia applications imposes strict quality of service (QoS) requirements on both access and core networks. These requirements and their impact to provide end-to-end 3D videoconferencing services have been studied within the Spanish-funded VISION project, where different scenarios were implemented showing an agile stereoscopic video call that might be offered to the general public in the near future. In view of the requirements, we designed an integrated access and core converged network architecture which provides the requested QoS to end-to-end IP sessions. Novel functional blocks are proposed to control core optical networks, the functionality of the standard ones is redefined, and the signaling improved to better meet the requirements of future multimedia services. An experimental test-bed to assess the feasibility of the solution was also deployed. In such test-bed, set-up and release of end-to-end sessions meeting specific QoS requirements are shown and the impact of QoS degradation in terms of the user perceived quality degradation is quantified. In addition, scalability results show that the proposed signaling architecture is able to cope with large number of requests introducing almost negligible delay.

Using the Journalistic Metaphor to Design User Interfaces That Explain Sensor Data

Facilitating general access to data from sensor networks (including traffic, hydrology and other domains) increases their utility. In this paper we argue that the journalistic metaphor can be effectively used to automatically generate multimedia presentations that help non-expert users analyze and understand sensor data. The journalistic layout and style are familiar to most users. Furthermore, the journalistic approach of ordering information from most general to most specific helps users obtain a high-level understanding while providing them the freedom to choose the depth of analysis to which they want to go. We describe the general characteristics and architectural requirements for an interactive intelligent user interface for exploring sensor data that uses the journalistic metaphor. We also describe our experience in developing this interface in real-world domains (e.g., hydrology).